Regression Models

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4/10 stars
based on  18 reviews
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Coursera online courses
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Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

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Humanities
Sciences & Technology
4715 reviews

Course Description

Learn how to use regression models, the most important statistical analysis tool in the data scientist's toolkit. This is the seventh course in the Johns Hopkins Data Science Specialization.
Reviews 4/10 stars
18 Reviews for Regression Models

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2/10 starsTaking Now
1 year, 9 months ago
just awful!!!! Not worth watching!! Definitely not worth paying for it !!!! I only did because I wanted to get the certificate. I got everything I needed to know from different classes
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2/10 starsTaking Now
2 years, 11 months ago
I think the instructor has been steered too far away from the objective of this course. This course is all about teaching students on how to apply the functions in R in achieving what we would need to analyse. This course is not about teaching students on how to produce a R library that can be used to ease R users' live. Therefore, explanation on formulas and proving of the formulas is not really necessary. He can probably develop a course on "How to develop R library" course which covers those low level materials. Please spend more time on explaining type of analysis we need to carry out and the relevant R function.
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2/10 starsTaking Now
  • 1 review
  • 0 completed
3 years, 6 months ago
Unintelligible lectures, even though I have some basic background in statistics. I have had to look up every single lecture topic in Regression Models elsewhere -- various YouTube tutorials and other written tutorials on the web -- to understand what the material in this course is really about. Prof. Caffo is obviously a smart guy, but he is way, way too theoretical. Very disappointing.
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2/10 starsCompleted
3 years, 9 months ago
I am sorry to say, but this and the "Statistical inference" are the worse courses in the whole "data science specialization" in my opinion. I understand that the topic is complicated, but I find the videos really unclear. There are better courses out there.
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4/10 starsTaking Now
3 years, 9 months ago
content not related to my field. assumed student have knowledge on stats concept. instructor just talk without having concern students understand or not.
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4/10 starsCompleted
3 years, 10 months ago
I agree with a comment elsewhere - this class should ideally be completely redone (with a different instructor). The emphasis is on derivation of formulas and techniques, not applications to the real world. Also, the course "textbook" is significantly inferior to the free OpenIntro textbook. Course quizzes and the project were unclearly specified and quirky in focus, while the project's peer grading was a bit random. I survived this course (and Inferential Statistics) by taking a normal university course on statistics simultaneously. Also, I followed up on this one by reviewing logistic regression in the OpenIntro textbook. As an aside, the Inferential Statistics and Regression Models courses seem almost completely detached from the rest of the sequence, which is a shame. The other instructors in the Data Science specialization are much more effective.
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8/10 starsCompleted
3 years, 11 months ago
The content is good. The instructor has tried to cover too much material for this course and the lectures are hard to follow because his speech is not confident and fluid. That makes following the ideas harder on material and ideas that are already complex.
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6/10 starsTaking Now
3 years, 12 months ago
We need a certification that would be the love child between Peng (on the R side) and Mine -From Duke- (on the statistical/regression side). That would make a very powerful course. Too bad they work on different institutions.
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8/10 starsTaking Now
4 years ago
I am currently taking this class and am finding the course quite challenging . I must say , that they have improved the standard and added new lectures which help you understand things better. I really appreciate the effort that the instructors are putting in the course , the new book on leanpub is really useful and the exercises after each chapter prepare you well for the quiz .
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2/10 starsTaking Now
4 years, 5 months ago
Sorry again Brian, Please consider letting Roger and Jeff rewrite this as peers and take it as a learning experience. I have a strong background already in math and data science in both practical and academic settings and find the lectures make my understanding worse! I have to go back and read other tutorials to remind myself of the basic intuitions you should be conveying and most importantly focus on R and the tools it provides, not the math proofs (just link to wikipedia for the proofs, they do them better already!) Regression is so crucial to the subject I won't even watch another video or read the lecture notes. Andrew Ng covered regression better in a single week than you did in four by providing simple, clear, practical intuitions. I believe you are competent in your field and probably very nice and I don't want to encourage you to quit, but to encourage you to have the courage to ask for your peers help and the have the... Sorry again Brian, Please consider letting Roger and Jeff rewrite this as peers and take it as a learning experience. I have a strong background already in math and data science in both practical and academic settings and find the lectures make my understanding worse! I have to go back and read other tutorials to remind myself of the basic intuitions you should be conveying and most importantly focus on R and the tools it provides, not the math proofs (just link to wikipedia for the proofs, they do them better already!) Regression is so crucial to the subject I won't even watch another video or read the lecture notes. Andrew Ng covered regression better in a single week than you did in four by providing simple, clear, practical intuitions. I believe you are competent in your field and probably very nice and I don't want to encourage you to quit, but to encourage you to have the courage to ask for your peers help and the have the humility to honestly learn from their help. Teaching is harder than math, harder than statistics and crucial to your job in this role.
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2/10 starsTaking Now
4 years, 5 months ago
The exposition in the videos is awful, that's unforgivable since they are taped. Brian, you should practice several times before recording the lecture. The exposition is sloppy.
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6/10 starsTaking Now
4 years, 5 months ago
overall I do like the way the data science specialisation is structured, but as many have said these courses are so boring, and the lecturers voice just makes me drift off to sleep. I have tried to listen to him for the past year, i enrol in the course every month and then give up!
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Marcelo Soares profile image
Marcelo Soares profile image
8/10 starsCompleted
  • 16 reviews
  • 13 completed
4 years, 7 months ago
Newly made videos for this installment of the course show a real improvement over the videos from the other course I took with him. Although the subject really demands more study, this time it seems the videos won't be a problem. Thank you a real lot. (UPDATED: new videos are available only for the first week and part of the second. Next installment's students will have it better.)
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5/10 starsDropped
4 years, 10 months ago
All the lectures need to be reworked. I recommend this book "Made to Stick". A great read to improve communication.
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Hamideh Iraj profile image
Hamideh Iraj profile image
2/10 starsCompleted
  • 70 reviews
  • 60 completed
4 years, 12 months ago
I read the reviews before starting the course and just did the exercises and course project with my prior knowledge. I think this course and statistical inference need to be rewritten. They are the weakest in the series.
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Richard Taylor profile image
Richard Taylor profile image
1/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years ago
Regression models is about fitting linear regression models to data. The lectures are bad, with many formulas that are not needed and take a lot of time and space that could be used to show better practical examples. There's a project where you have to apply what you supposedly learned. The project is quite fun to do but you have to research and learn the methods yourself. Regression models is quite a fun topic that is badly presented in this course.
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Greg Hamel profile image
Greg Hamel profile image
3/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 2 months ago
Regression Models is the 7th course in the John Hopkins data science specialization track on Coursera. This course is essentially identical to the statistical inference course in terms of structure, presentation and quality: the entire course consists of dull, information-packed slides with mediocre voice-overs. It seems like half of the course consists of slides with verbose math expressions in summation notation and the instructor telling you don't really need to understand them unless you are interested in the math behind the models. As with other courses in the track, there are no in-lecture quizzes or interactive exercises and there is no instructor face time. Overall this is a disappointing course that probably won’t keep your interest long enough for you to bother completing all the videos much less the quizzes and the project. If you’re looking for other places to learn about regression models, the last two weeks of Duke'... Regression Models is the 7th course in the John Hopkins data science specialization track on Coursera. This course is essentially identical to the statistical inference course in terms of structure, presentation and quality: the entire course consists of dull, information-packed slides with mediocre voice-overs. It seems like half of the course consists of slides with verbose math expressions in summation notation and the instructor telling you don't really need to understand them unless you are interested in the math behind the models. As with other courses in the track, there are no in-lecture quizzes or interactive exercises and there is no instructor face time. Overall this is a disappointing course that probably won’t keep your interest long enough for you to bother completing all the videos much less the quizzes and the project. If you’re looking for other places to learn about regression models, the last two weeks of Duke's data analysis and statistical inference cover regression, as do the first few weeks of MIT's Analytics Edge. I highly recommend both of those courses. Regression Models does cover regression in a bit more detail, but given the poor presentation you'd probably be better off reading Wikipedia. *Update: John Hopkins has recently released an interactive learning package for R called Swirl that provides a series of exercises for this course and some of their other Coursera offerings. The Swirl exercises for this course help reinforce the topics in a way that is much more engaging than the lectures. I give the Swirl exercises for this course a score of 3/5 stars. It would have been nice if the Swirl package was available from the beginning.
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Jeff Winchell profile image
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2/10 starsDropped
  • 91 reviews
  • 66 completed
4 years, 6 months ago
I have to agree with the first reviewer about the quality of this course. It is quite irritating to listen to the lecturer stop and go back, get lost in which slide he is in, insert way too many ums and pauses, etc. He could learn how to edit and retape his audio. All the long-winded theoretical talk is quite boring. I tried hard to soldier on to complete this course, just for the sake of completing but couldn't take the professor any more so I dropped it.
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